BloodCancerMultiOmics2017 - data overview
28 August 2018
1 Prerequisites
library("BloodCancerMultiOmics2017")
# additional
library("Biobase")
library("SummarizedExperiment")
library("DESeq2")
library("reshape2")
library("ggplot2")
library("tidyverse")
library("BiocStyle")2 Introduction
Primary tumor samples from blood cancer patients underwent functional and molecular characterization. BloodCancerMultiOmics2017 includes the resulting preprocessed data. A quick overview of the available data is provided below. For the details on experimental settings please refer to:
S Dietrich*, M Oleś*, J Lu* et al. Drug-perturbation-based stratification of blood cancer
J. Clin. Invest. (2018); 128(1):427–445. doi:10.1172/JCI93801.
* equal contribution
3 Data overview
Load all of the available data.
data("conctab", "drpar", "lpdAll", "patmeta", "day23rep", "drugs",
"methData", "validateExp", "dds", "exprTreat", "mutCOM",
"cytokineViab")The data sets are objects of different classes (data.frame, ExpressionSet, NChannelSet, RangedSummarizedExperiment, DESeqDataSet), and include data for either all studied patient samples or only a subset of these. The overview below shortly describes and summarizes the data available. Please note that the presence of a given patient sample ID within the data set doesn’t necessarily mean that the data is available for this sample (the slot could be filled with NAs).
Patient samples per data set.
samplesPerData = list(
drpar = colnames(drpar),
lpdAll = colnames(lpdAll),
day23rep = colnames(day23rep),
methData = colnames(methData),
patmeta = rownames(patmeta),
validateExp = unique(validateExp$patientID),
dds = colData(dds)$PatID,
exprTreat = unique(pData(exprTreat)$PatientID),
mutCOM = rownames(mutCOM),
cytokineViab = unique(cytokineViab$Patient)
)List of all samples present in data sets.
(samples = sort(unique(unlist(samplesPerData))))## [1] "H001" "H002" "H003" "H005" "H006" "H007" "H008" "H009" "H010" "H011"
## [11] "H012" "H013" "H014" "H015" "H016" "H017" "H018" "H019" "H020" "H021"
## [21] "H022" "H023" "H024" "H025" "H026" "H027" "H028" "H029" "H030" "H031"
## [31] "H032" "H033" "H035" "H036" "H037" "H038" "H039" "H040" "H041" "H042"
## [41] "H043" "H044" "H045" "H046" "H047" "H048" "H049" "H050" "H051" "H053"
## [51] "H054" "H055" "H056" "H057" "H058" "H059" "H060" "H062" "H063" "H064"
## [61] "H065" "H066" "H067" "H069" "H070" "H071" "H072" "H073" "H074" "H075"
## [71] "H076" "H077" "H078" "H079" "H080" "H081" "H082" "H083" "H084" "H085"
## [81] "H086" "H087" "H088" "H089" "H090" "H092" "H093" "H094" "H095" "H096"
## [91] "H097" "H098" "H099" "H100" "H101" "H102" "H103" "H104" "H105" "H106"
## [101] "H107" "H108" "H109" "H110" "H111" "H112" "H113" "H114" "H115" "H116"
## [111] "H117" "H118" "H119" "H120" "H121" "H122" "H126" "H127" "H128" "H133"
## [121] "H134" "H135" "H136" "H137" "H140" "H141" "H142" "H143" "H144" "H145"
## [131] "H146" "H147" "H148" "H149" "H150" "H151" "H152" "H153" "H154" "H155"
## [141] "H156" "H157" "H158" "H159" "H160" "H161" "H162" "H163" "H164" "H165"
## [151] "H166" "H167" "H168" "H169" "H170" "H171" "H172" "H173" "H174" "H175"
## [161] "H176" "H177" "H178" "H179" "H180" "H181" "H182" "H183" "H184" "H185"
## [171] "H186" "H187" "H188" "H189" "H190" "H191" "H192" "H193" "H194" "H195"
## [181] "H196" "H197" "H198" "H199" "H200" "H201" "H202" "H203" "H204" "H205"
## [191] "H206" "H207" "H208" "H209" "H210" "H211" "H212" "H213" "H214" "H215"
## [201] "H216" "H217" "H218" "H219" "H220" "H221" "H222" "H223" "H224" "H225"
## [211] "H226" "H227" "H228" "H229" "H230" "H231" "H232" "H233" "H234" "H235"
## [221] "H236" "H237" "H238" "H239" "H240" "H241" "H242" "H243" "H244" "H245"
## [231] "H246" "H247" "H248" "H249" "H250" "H251" "H252" "H253" "H254" "H255"
## [241] "H256" "H257" "H258" "H259" "H260" "H261" "H262" "H263" "H264" "H265"
## [251] "H266" "H267" "H268" "H269" "H270" "H271" "H272" "H273" "H274" "H275"
## [261] "H276" "H277" "H278" "H279" "H280" "H281" "H282" "H283" "MNC5" "MNC6"
## [271] "MNC7"Total number of samples.
length(samples)## [1] 271A plot summarizing the presence of a given patient sample within each data set.
The classification below stratifies data sets according to different types of experiments performed and included. Please refer to the manual for a more detailed information on the content of these data objects.
3.1 Patient metadata
Patient metadata is provided in the patmeta object.
# Number of patients per disease
sort(table(patmeta$Diagnosis), decreasing=TRUE)##
## CLL T-PLL MCL MZL AML LPL B-PLL HCL
## 200 25 10 6 5 4 3 3
## hMNC HCL-V Sezary FL PTCL-NOS
## 3 2 2 1 1# Number of samples from pretreated patients
table(!patmeta$IC50beforeTreatment)##
## FALSE TRUE
## 131 52# IGHV status of CLL patients
table(patmeta[patmeta$Diagnosis=="CLL", "IGHV"])##
## M U
## 104 843.2 High-throughput drug screen data
The viability measurements from the high-throughput drug screen are included in the drpar object. The metadata about the drugs and drug concentrations used can be found in drugs and conctab objects, respectively.
The drpar object includes multiple channels, each of which consists of cells’ viability data for a single drug concentration step. Channels viaraw.1_5 and viaraw.4_5 contain the mean viability score between multiple concentration steps as indicated at the end of the channel name.
channelNames(drpar)## [1] "viaraw.1" "viaraw.1_5" "viaraw.2" "viaraw.3" "viaraw.4"
## [6] "viaraw.4_5" "viaraw.5"# show viability data for the first 5 patients and 7 drugs in their lowest conc.
assayData(drpar)[["viaraw.1"]][1:7,1:5]## H001 H002 H003 H005 H009
## D_001 0.3283159 0.2632593 0.4969538 0.02410373 0.19080155
## D_002 0.8322242 1.0353523 0.8381608 0.16190086 0.41572819
## D_003 0.7832597 0.8428674 0.7449635 0.65899478 0.41898375
## D_004 0.6529834 0.5654266 0.3548642 0.02827099 0.04094826
## D_006 0.4779778 0.6871057 0.4894785 0.30327813 0.12296120
## D_007 0.5912538 0.7246273 0.5119393 0.10447502 0.15369103
## D_008 0.3728575 0.5261570 0.3161154 0.02266465 0.03423222Drug metadata.
# number of drugs
nrow(drugs)## [1] 91# type of information included in the object
colnames(drugs)## [1] "name" "main_targets" "target_category" "group"
## [5] "pathway" "distributor" "approved_042016" "devel_042016"Drug concentration steps (c1 - lowest, c5 - highest).
head(conctab)## c1 c2 c3 c4 c5
## D_001 1 0.25 0.063 0.016 0.004
## D_002 40 10.00 2.500 0.625 0.156
## D_003 40 10.00 2.500 0.625 0.156
## D_004 4 1.00 0.250 0.063 0.016
## D_006 40 10.00 2.500 0.625 0.156
## D_007 20 5.00 1.250 0.313 0.078The reproducibility of the screening platform was assessed by screening 3 patient samples in two replicates. The viability measurements are available for two time points: 48 h and 72 h after adding the drug. The screen was performed for 67 drugs in 1-2 different drug concentrations (16 in 1 and 51 in 2 drug concentrations). This data is provided in day23rep.
channelNames(day23rep)## [1] "day2rep1" "day2rep2" "day3rep1" "day3rep2"# show viability data for 48 h time point for all patients marked as
# replicate 1 and 3 first drugs in all their conc.
drugs2Show = unique(fData(day23rep)$DrugID)[1:3]
assayData(day23rep)[["day2rep1"]][fData(day23rep)$DrugID %in% drugs2Show,]## H016 H022 H030
## D_001-0.05 0.6037813 0.3461988 0.7801167
## D_001-0.1 0.2637692 0.2190428 0.6309255
## D_002-1 0.9959188 0.8119468 0.9005653
## D_002-10 0.9381762 0.4948085 0.8112993
## D_003-1 0.9575617 0.6848139 0.8939423
## D_003-10 0.7988831 0.5007344 0.8348084The follow-up drug screen, which confirmed the targets and the signaling pathway dependence of the patient samples was performed for 128 samples and the following drugs: Cobimetinib, Ganetespib, Onalespib, SCH772984, Trametinib.
| Drug name | Target |
|---|---|
| Cobimetinib | MEK |
| Trametinib | MEK |
| SCH772984 | ERK1/2 |
| Ganetespib | Hsp90 |
| Onalespib | Hsp90 |
The data is included in the validateExp object.
head(validateExp)## # A tibble: 6 x 4
## patientID Drug Concentration viab
## <chr> <chr> <dbl> <dbl>
## 1 H005 Cobimetinib 0.0032 0.749
## 2 H005 Cobimetinib 0.016 0.726
## 3 H005 Cobimetinib 0.08 0.723
## 4 H005 Cobimetinib 0.4 0.732
## 5 H005 Cobimetinib 2 0.744
## 6 H005 Ganetespib 0.0032 1.00Moreover, we also performed a small drug screen in order to check the influence of the different cytokines/chemokines on the viability of the samples. These data are included in cytokineViab object.
head(cytokineViab)## # A tibble: 6 x 11
## Patient Timepoint Recording_date Seeding_date Stimulation Cytokine_Concen…
## <chr> <chr> <date> <date> <chr> <fct>
## 1 H060 48h 2017-06-02 2017-05-31 IL-2 c1
## 2 H060 48h 2017-06-02 2017-05-31 IL-2 c2
## 3 H060 48h 2017-06-02 2017-05-31 IL-2 c3
## 4 H060 48h 2017-06-02 2017-05-31 IL-4 c1
## 5 H060 48h 2017-06-02 2017-05-31 IL-4 c2
## 6 H060 48h 2017-06-02 2017-05-31 IL-4 c3
## # ... with 5 more variables: Duplicate <dbl>, Normalized_DMSO <dbl>,
## # mtor <chr>, Edge <fct>, Cytokine_Concentration2 <chr>3.3 Gene mutation data
The mutCOM object contains information on the presence of gene mutations in the studied patient samples.
# there is only one channel with the binary type of data for each gene
channelNames(mutCOM)## [1] "binary"# the feature data includes detailed information about mutations in
# TP53 and BRAF genes, as well as clone size of
#del17p13, KRAS, UMODL1, CREBBP, PRPF8, trisomy12 mutations
colnames(fData(mutCOM))## [1] "TP53_CDS" "TP53_AA" "TP53_%" "TP53cs" "BRAF_CDS"
## [6] "BRAF_AA" "BRAF_%" "BRAFcs" "del17p13cs" "KRAScs"
## [11] "UMODL1cs" "CREBBPcs" "PRPF8cs" "trisomy12cs"3.4 Gene expression data
RNA-Seq data preprocessed with DESeq2 is provided in the dds object.
# show count data for the first 5 patients and 7 genes
assay(dds)[1:7,1:5]## 1 2 3 4 6
## ENSG00000000003 0 0 9 0 2
## ENSG00000000005 0 0 0 0 0
## ENSG00000000419 1272 3443 1682 1574 3463
## ENSG00000000457 430 472 2285 1022 1030
## ENSG00000000460 270 1043 1882 760 830
## ENSG00000000938 9391 24963 40337 31327 10077
## ENSG00000000971 5 1 57 56 0# show the above with patient sample ids
assay(dds)[1:7,1:5] %>% `colnames<-` (colData(dds)$PatID[1:5])## H045 H109 H024 H056 H079
## ENSG00000000003 0 0 9 0 2
## ENSG00000000005 0 0 0 0 0
## ENSG00000000419 1272 3443 1682 1574 3463
## ENSG00000000457 430 472 2285 1022 1030
## ENSG00000000460 270 1043 1882 760 830
## ENSG00000000938 9391 24963 40337 31327 10077
## ENSG00000000971 5 1 57 56 0# number of genes and patient samples
nrow(dds); ncol(dds)## [1] 63677## [1] 136Additionally, 12 patient samples underwent gene expression profiling using Illumina microarrays before and 12 h after treatment with 5 drugs. These data are included in the exprTreat data object.
# patient samples included in the data set
(p = unique(pData(exprTreat)$PatientID))## [1] "H112" "H108" "H238" "H194" "H173" "H234" "H169" "H233" "H094" "H109"
## [11] "H114" "H167"# type of metadata included for each gene
colnames(fData(exprTreat))## [1] "ProbeID" "TargetID" "QC"
## [4] "Species" "Source" "Search_Key"
## [7] "Transcript" "ILMN_Gene" "Source_Reference_ID"
## [10] "RefSeq_ID" "Unigene_ID" "Entrez_Gene_ID"
## [13] "GI" "Accession" "Symbol"
## [16] "Protein_Product" "Probe_Id" "Array_Address_Id"
## [19] "Probe_Type" "Probe_Start" "Probe_Sequence"
## [22] "Chromosome" "Probe_Chr_Orientation" "Probe_Coordinates"
## [25] "Cytoband" "Definition" "Ontology_Component"
## [28] "Ontology_Process" "Ontology_Function" "Synonyms"
## [31] "Obsolete_Probe_Id" "Factor"# show expression level for the first patient and 3 first probes
Biobase::exprs(exprTreat)[1:3, pData(exprTreat)$PatientID==p[1]]## 200128470091_A 200128470091_B 200128470091_C 200128470091_D
## R00001 9.172930 9.086734 9.112330 9.202076
## R00002 4.931757 4.893040 4.439497 4.887295
## R00003 4.887619 4.668925 4.796266 5.298392
## 200128470091_E 200128470091_F
## R00001 9.073740 8.870751
## R00002 5.227140 5.379562
## R00003 5.038028 5.1151413.5 DNA methylation data
DNA methylation included in methData object contains data for 196 patient samples and 5000 of the most variable CpG sites.
# show the methylation for the first 7 CpGs and the first 5 patient samples
assay(methData)[1:7,1:5]## H005 H023 H006 H024 H010
## cg17479716 0.13512727 0.94295266 0.03052286 0.03086889 0.97261914
## cg00674365 0.97992788 0.02102404 0.98030338 0.97448056 0.06720939
## cg24299136 0.97637954 0.97179489 0.03779711 0.08808060 0.97364936
## cg18723409 0.03438240 0.57677823 0.96091989 0.96505311 0.07195807
## cg23844018 0.03451902 0.97407334 0.10804103 0.17808162 0.97309712
## cg08425796 0.04653624 0.95438826 0.05756599 0.11993044 0.94959487
## cg02956248 0.40450644 0.96655136 0.18370129 0.03276800 0.96370039# type of metadata included for CpGs
colnames(rowData(methData))## [1] "Strand" "Name"
## [3] "AddressA" "AddressB"
## [5] "ProbeSeqA" "ProbeSeqB"
## [7] "Type" "NextBase"
## [9] "Color" "Probe_rs"
## [11] "Probe_maf" "CpG_rs"
## [13] "CpG_maf" "SBE_rs"
## [15] "SBE_maf" "Islands_Name"
## [17] "Relation_to_Island" "Forward_Sequence"
## [19] "SourceSeq" "Random_Loci"
## [21] "Methyl27_Loci" "UCSC_RefGene_Name"
## [23] "UCSC_RefGene_Accession" "UCSC_RefGene_Group"
## [25] "Phantom" "DMR"
## [27] "Enhancer" "HMM_Island"
## [29] "Regulatory_Feature_Name" "Regulatory_Feature_Group"
## [31] "DHS"# number of patient samples screened with the given platform type
table(colData(methData)$platform)##
## 450k 850k
## 118 783.6 Other
Object lpdAll is a convenient assembly of data contained in the other data objects mentioned earlier in this vignette. For details, please refer to the manual.
# number of rows in the dataset for each type of data
table(fData(lpdAll)$type)##
## IGHV Methylation_Cluster gen
## 1 1 89
## viab
## 448# show viability data for drug ibrutinib, idelalisib and dasatinib
# (in the mean of the two lowest concentration steps) and
# the first 5 patient samples
Biobase::exprs(lpdAll)[which(
with(fData(lpdAll),
name %in% c("ibrutinib", "idelalisib", "dasatinib") &
subtype=="4:5")), 1:5]## H005 H009 H010 H011 H012
## D_002_4:5 0.9915251 1.0966397 0.8994378 1.0815918 0.9402181
## D_003_4:5 1.0253976 0.9924037 0.8850690 1.0352433 0.9109271
## D_050_4:5 0.9857736 0.6161776 0.6717888 0.8889409 0.61515464 Original data
The raw data from the whole exome sequencing, RNA-seq and DNA methylation arrays is stored in the European Genome-Phenome Archive (EGA) under accession number EGAS0000100174.
The preprocesed DNA methylation data, which include complete list of CpG sites (not only the 5000 with the highest variance) can be accessed through Bioconductor ExperimentHub platform.
library("ExperimentHub")
eh = ExperimentHub()
obj = query(eh, "CLLmethylation")
meth = obj[["EH1071"]] # extract the methylation data5 Session info
sessionInfo()## R version 3.5.1 Patched (2018-07-12 r74967)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.7-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.7-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] forcats_0.3.0 stringr_1.3.1
## [3] dplyr_0.7.6 purrr_0.2.5
## [5] readr_1.1.1 tidyr_0.8.1
## [7] tibble_1.4.2 tidyverse_1.2.1
## [9] ggplot2_3.0.0 reshape2_1.4.3
## [11] DESeq2_1.20.0 SummarizedExperiment_1.10.1
## [13] DelayedArray_0.6.5 BiocParallel_1.14.2
## [15] matrixStats_0.54.0 GenomicRanges_1.32.6
## [17] GenomeInfoDb_1.16.0 IRanges_2.14.11
## [19] S4Vectors_0.18.3 Biobase_2.40.0
## [21] BiocGenerics_0.26.0 BloodCancerMultiOmics2017_1.0.2
## [23] BiocStyle_2.8.2
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.3-2 rprojroot_1.3-2 htmlTable_1.12
## [4] XVector_0.20.0 base64enc_0.1-3 ggdendro_0.1-20
## [7] rstudioapi_0.7 bit64_0.9-7 fansi_0.3.0
## [10] AnnotationDbi_1.42.1 lubridate_1.7.4 xml2_1.2.0
## [13] codetools_0.2-15 splines_3.5.1 geneplotter_1.58.0
## [16] knitr_1.20 Formula_1.2-3 jsonlite_1.5
## [19] broom_0.5.0 annotate_1.58.0 cluster_2.0.7-1
## [22] compiler_3.5.1 httr_1.3.1 backports_1.1.2
## [25] assertthat_0.2.0 Matrix_1.2-14 lazyeval_0.2.1
## [28] cli_1.0.0 acepack_1.4.1 htmltools_0.3.6
## [31] tools_3.5.1 bindrcpp_0.2.2 gtable_0.2.0
## [34] glue_1.3.0 GenomeInfoDbData_1.1.0 ipflasso_0.1
## [37] Rcpp_0.12.18 cellranger_1.1.0 nlme_3.1-137
## [40] iterators_1.0.10 xfun_0.3 rvest_0.3.2
## [43] devtools_1.13.6 XML_3.98-1.16 zlibbioc_1.26.0
## [46] MASS_7.3-50 scales_1.0.0 hms_0.4.2
## [49] RColorBrewer_1.1-2 yaml_2.2.0 memoise_1.1.0
## [52] gridExtra_2.3 rpart_4.1-13 latticeExtra_0.6-28
## [55] stringi_1.2.4 RSQLite_2.1.1 genefilter_1.62.0
## [58] foreach_1.4.4 checkmate_1.8.5 rlang_0.2.2
## [61] pkgconfig_2.0.2 bitops_1.0-6 evaluate_0.11
## [64] lattice_0.20-35 bindr_0.1.1 htmlwidgets_1.2
## [67] bit_1.1-14 tidyselect_0.2.4 plyr_1.8.4
## [70] magrittr_1.5 bookdown_0.7 R6_2.2.2
## [73] Hmisc_4.1-1 DBI_1.0.0 pillar_1.3.0
## [76] haven_1.1.2 foreign_0.8-71 withr_2.1.2
## [79] survival_2.42-6 RCurl_1.95-4.11 nnet_7.3-12
## [82] modelr_0.1.2 crayon_1.3.4 utf8_1.1.4
## [85] rmarkdown_1.10 locfit_1.5-9.1 grid_3.5.1
## [88] readxl_1.1.0 data.table_1.11.4 blob_1.1.1
## [91] digest_0.6.16 xtable_1.8-2 munsell_0.5.0
## [94] glmnet_2.0-16 beeswarm_0.2.3